Data visualization in R Using ggplot2
Surya Lamichhane
01 March 2025
By the end of this lesson, you will be able to:
- Visualize data using ggplot function from ggplot2 package
- Describe and Analyze date using ggplot functions
Part 2. (Data vizulization in R using ggplot)
ggplot2 is one of the most used packages for data visualization in R and it builds plots in layers.
ggplot2 builds graphs in layers. It divides the plot into three parts:
- Data: It is the dataframe that contains data to be plotted.
- Aesthetics: These are the variables mapping to the visual properties of the plot.
- Geometry: It refers to the type of graph that is used, such as bar graph or histogram
- Optional parts:
- XY- axex label
- theme
For more details see the link [http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html]
- syntax
ggplot(data) +
aes(x, y) +
geom_..() + # geometry type such point, hist, bar
optional_layer # such as theme, xy-labels
-OR- (using dplyr pyping)
data %>%
ggplot() %>%
aes(x, y) +
geom_..() 2.1 Scatter plot
# install.packages("ggplot2")
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)iris %>%
ggplot() +
aes(x = Sepal.Length, y = Sepal.Width) +
#geom_point(aes(size=Sepal.Length)) # size of points varies as values
geom_point(size=5, shape=2) 
2.1.1. Label in the scatter plot
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) + # Points color by Species
geom_point() +
xlab("Sepal Length") + # X-axis label
ylab("Sepal Width") + # Y-axis label
theme(axis.line = element_line(colour = "black", # Changes the default theme
linewidth =0.5))
2.1.2. Plot Title and Change legend name
library(ggplot2)
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
geom_point(size = 2) + # Increased point size for better visibility
ggtitle("Scatter Plot in R") +
scale_color_discrete(name = "Iris Species") + # Correct way to rename legend
xlab("Sepal Length") +
ylab("Sepal Width") +
theme(
axis.line = element_line(colour = "red", size = 0.5), # Keep red xy-axes
plot.title = element_text(hjust = 0.5, size = 20, face = "bold"), # Centered and bold title
)## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
2.1.3. Straigh line using abline()
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
geom_point() +
geom_abline(intercept = 3, slope = 0 ) + # draw a horizontal line
ggtitle("Scattor plot in R") +
scale_color_discrete(name = "Species") + # Legend title
xlab("Sepal.Length") + # X-axis label
ylab("Sepal.Width") + # Y-axis label
theme(axis.line = element_line(color = "black", size = 0.5), # Changes the default theme (xy-axes)
plot.title = element_text(hjust=0.5, size = 20, face = "bold")# Assign title on center
)
2.1.4. Remove the grids and change background to white
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
geom_point(size = 2) + # Adds scatter points with color by Species
ggtitle("Scatter Plot in R") +
scale_color_discrete(name = "Species") + # Legend title
xlab("Sepal Length") +
ylab("Sepal Width") +
theme(
axis.line = element_line(colour = "black", size = 0.5), # Make xy-axes black
plot.title = element_text(hjust = 0.5, face = "bold", size = 20), # Center title, bold, increase size
panel.background = element_rect(fill = "white"), # White background
panel.grid.major = element_blank(), # Remove major grid lines
panel.grid.minor = element_blank() # Remove minor grid lines
)
2.2. Line plots in R
# Change the line type
ggplot(data=iris, aes(x = Sepal.Length, y = Sepal.Width)) +
geom_line(linetype = "dashed")
# add points
ggplot(data=iris, aes(x = Sepal.Length, y = Sepal.Width)) +
geom_line(linetype = "solid")+
geom_point()
# Line plot grouped by Species
ggplot(data = iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
geom_line() + # Line plot grouped by Species
geom_point(size = 2) + # Scatter points with color by Species
ggtitle("Sepal Length vs Sepal Width by Species") + # Title
scale_color_discrete(name = "Species") + # Properly label legend
xlab("Sepal Length") +
ylab("Sepal Width") +
theme(
axis.line = element_line(colour = "black", size = 0.5), # Make xy-axes black, size is thickness
plot.title = element_text(hjust = 0.5, size = 18, face = "bold"), # Centered bold title
legend.position = "right", # Position legend to the right
panel.background = element_rect(fill = "white"), # White background
panel.grid.major = element_blank(), # Remove major grid lines
panel.grid.minor = element_blank() # Remove minor grid lines
)
2.3. Bar plot in R
str(mtcars)## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...2.3.1. Creating a Bar Plot with Factor Variables in
mtcars %>%
ggplot(aes(x = factor(cyl), fill = factor(cyl))) + # x varaible must be factor, y is not required will be automatically counted.
geom_bar() +
scale_fill_discrete(name = "Cylinders") + # Change Legend title
ggtitle("Bar Plot of Cylinder Counts") +
xlab("Number of Cylinders") +
ylab("Count") +
theme_minimal()+
theme(
#axis.line = element_line(colour = "black", size = 0.5), # Make xy-axes black, size is thickness
plot.title = element_text(hjust = 0.5, size = 18, face = "bold"), # Centered bold title
)
2.3.2. Creating a Bar Plot when we know the frequency (counts) of the labels
df = mtcars %>%
group_by(cyl)%>%
summarise(count = n())
df## # A tibble: 3 × 2
## cyl count
## <dbl> <int>
## 1 4 11
## 2 6 7
## 3 8 14- In ggplot2, the geom_bar() function has a default statistical transformation (stat = “count”), which automatically counts occurrences of categories when no y variable is specified.
- However, if we already have pre-computed values (such as exact counts or aggregated data) and want to use them directly, we set stat = “identity” to ensure the bars represent the actual y values provided.
# Basic barplot
df$cyl = factor(df$cyl) # changing to factor is important here, otherwise R will consider the values are in continuous scale
# Create the bar plot
ggp <- ggplot(data = df, aes(x = cyl, y = count, fill = cyl)) +
geom_bar(stat = "identity", width = 0.7) + # Identity means use precomputed counts
scale_fill_discrete(name = "Cylinders") + # Update legend title
ggtitle("Bar Plot of Cylinder Counts") +
xlab("Number of Cylinders") +
ylab("Count") +
theme_minimal() + # White background
theme(
plot.title = element_text(hjust = 0.5, size = 18, face = "bold") # Centered bold title
)
# Display the plot
print(ggp)
2.3.4 Horizontal Barplot
# for the summary x variable need to be a factor variable
ggp <- ggplot(data = df, aes(y = reorder(cyl,count), x = count, fill = cyl)) +
geom_bar(stat="identity", width=0.8) +
scale_fill_discrete(name = "Cylinders") + # Update legend title
ggtitle("Bar Plot of Cylinder Counts") +
ylab("Number of Cylinders") +
xlab("Count") +
theme_minimal() + # White background
theme(
plot.title = element_text(hjust = 0.5, size = 18, face = "bold") # Centered bold title
)
ggp
2.3.5. Application to the Boston-311 Service Data
Boston311_2023_data =
read.csv("https://data.boston.gov/dataset/8048697b-ad64-4bfc-b090-ee00169f2323/resource/e6013a93-1321-4f2a-bf91-8d8a02f1e62f/download/tmp518q5snq.csv")library(stringr)
library(dplyr)
Boston311_2023_data$Parking_Enforcement_status <- str_detect(Boston311_2023_data$case_title,
regex("\\bParking Enforcement\\b"))
Parking_Enforcement_by_nbd <- Boston311_2023_data %>%
group_by(neighborhood) %>%
summarise(nbd_count_Parking_Enforcement = n()) %>%
arrange(desc(nbd_count_Parking_Enforcement))
head(Parking_Enforcement_by_nbd, 10)## # A tibble: 10 × 2
## neighborhood nbd_count_Parking_Enforcement
## <chr> <int>
## 1 Dorchester 36272
## 2 Roxbury 21426
## 3 South Boston / South Boston Waterfront 18835
## 4 Allston / Brighton 18490
## 5 East Boston 17862
## 6 South End 15265
## 7 Jamaica Plain 13728
## 8 Downtown / Financial District 11526
## 9 Greater Mattapan 11191
## 10 Back Bay 10559top_10_nbd = Parking_Enforcement_by_nbd[1:10, ]
top_10_nbd## # A tibble: 10 × 2
## neighborhood nbd_count_Parking_Enforcement
## <chr> <int>
## 1 Dorchester 36272
## 2 Roxbury 21426
## 3 South Boston / South Boston Waterfront 18835
## 4 Allston / Brighton 18490
## 5 East Boston 17862
## 6 South End 15265
## 7 Jamaica Plain 13728
## 8 Downtown / Financial District 11526
## 9 Greater Mattapan 11191
## 10 Back Bay 10559ggp_311 <- ggplot(top_10_nbd, aes(y = reorder(neighborhood, nbd_count_Parking_Enforcement),
x = nbd_count_Parking_Enforcement,
fill = neighborhood)) +
geom_bar(stat = "identity") +
scale_fill_discrete(name = "Neighborhood") + # Corrected legend title
ggtitle("Top 10 Neighborhoods by Parking Enforcement Count") + # Added title
xlab("Parking Enforcement Count") +
ylab("Neighborhood") +
theme_minimal() + # Clean background instead of theme_void()
theme(
plot.title = element_text(hjust = 0.5, size = 16, face = "bold"), # Centered, bold title
legend.position = "none" # Removes the legend
)
ggp_311
2.3.5. Saving Plots
ggsave("~/Desktop/NBD_complaint.png") # save in desktop with file name "NBD_complaint"## Saving 7 x 5 in image2.4. Stacked bar plot
- Creating Single Stacked bar plot is exactly similar as bar-graph, but we leave x as x = ” ” (empty).
# Create bar plot
ggp <- ggplot(data = mtcars, aes(x = '', fill = factor(cyl))) +
geom_bar(stat = "count", width = 0.5) +
scale_fill_discrete(name = "Cylinders") + # Corrected legend title
ggtitle("Cylinder Count in mtcars Dataset") +
xlab("Cylinders") +
ylab("Count") +
theme_minimal()+
theme(
axis.line = element_line(colour = "black", size = 0.5), # Make xy-axes black, size is thickness
plot.title = element_text(hjust = 0.5, size = 18, face = "bold") # Centered bold title
)
ggp
- Multiple stacked bar plot based on cylinder and gears
library(ggplot2)
ggplot(mtcars, aes(x = factor(cyl), fill = factor(gear))) +
geom_bar() +
scale_fill_discrete(name = "gears") + # Corrected legend title
ggtitle("Subdivided (Stacked) Bar Plot") +
xlab("Number of Cylinders") +
ylab("Count") +
theme_minimal()+
theme(
axis.line = element_line(colour = "black", size = 0.5), # Make xy-axes black, size is thickness
plot.title = element_text(hjust = 0.5, size = 18, face = "bold") # Centered bold title
)
2.5. Pie chart
Creating a Pie Chart in ggplot2 is similar to a subdivided bar chart (stacked bar chart), but we need to add an additional layer using coord_polar(theta = “y”) to transform it into a circular shape.
df <- mtcars %>%
group_by(cyl) %>%
summarize(count = n()) %>% # count the number of cars basedd on cylinders
mutate(cyl = factor(cyl)) # convert to factor varaible
df## # A tibble: 3 × 2
## cyl count
## <fct> <int>
## 1 4 11
## 2 6 7
## 3 8 14ggp <- ggplot(data=df, aes(x = '', y = count, fill = cyl)) +
geom_bar(stat="identity", width=0.7) +
coord_polar("y", start=0)
ggp
Let’s add the percentage in each sector
count_percent <- df$count/sum(df$count) *100
df$perc = round(count_percent, 2)
ggp <- ggplot(data=df, aes(x = '', y = perc, fill = cyl)) +
geom_bar(stat="identity", width=0.7) +
coord_polar("y", start=0)+
scale_fill_discrete(name = "Cylinders") + # Corrected legend title
geom_text(aes(label = paste(perc, '%')), color = rep("white", 3),
position = position_stack(vjust = 0.5)) +
theme_void()
ggp
2.6. Histogram in R
#?geom_histogram()2.6.1. Basic histogram
ggplot(iris, aes(x=Sepal.Length)) +
geom_histogram(bins = 20) # number of bins 20, you can change this
# Change the width of bins
ggplot(iris, aes(x=Sepal.Length)) +
geom_histogram(binwidth=0.3,bins = 20)
# Change colors
#` color means boundary color
#` fill means color fill inside the geometrical object
p <-ggplot(iris, aes(x=Sepal.Length)) +
geom_histogram(binwidth=0.3,bins = 20, color="black", fill="gray")+
theme_void()
p
Add mean line and density plot on the histogram
# Add mean line
p + geom_vline(aes(xintercept=mean(Sepal.Length)),
color="blue", linetype="dashed", size=1)## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
2.6.2. Histogram with density plot
ggplot(iris, aes(Sepal.Length)) +
geom_histogram(aes(y = ..density..),
color = "black", fill = "white", alpha = 0.5, bins = 30) + # Adjust transparency and bin size
geom_density(color = "red", fill = "grey", alpha = 0.2) + # Density curve with transparency
ggtitle("Histogram with Density Plot") +
xlab("Sepal Length") +
ylab("Density") +
theme_minimal() +
theme(
plot.title = element_text(hjust = 0.5, size = 16, face = "bold") # Centered title
)## Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(density)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
2.6.3. Histogram of a varaible with multiple groups
# Change histogram plot line colors by groups
ggplot(iris, aes(x = Sepal.Length, color = Species, fill = Species)) +
geom_histogram(alpha = 0.5, position = "identity", bins = 20) + # Adjust transparency & bins
ggtitle("Histogram of Sepal Length by Species") +
xlab("Sepal Length") +
ylab("Count") +
theme_minimal() +
theme(
plot.title = element_text(hjust = 0.5, size = 16, face = "bold") # Centered title
)
2.7. Box plot
# Basic box plot
p <- ggplot(iris, aes(Sepal.Length)) +
geom_boxplot()
p
# Horizontal box plot
p + coord_flip()
#box plot for multiple category
ggplot(iris, aes(x=Sepal.Length, y=Species)) +
geom_boxplot()
# Notched box plot
ggplot(iris, aes(x=Sepal.Length, y=Species)) +
geom_boxplot(notch=TRUE)
# Change outlier, color, shape and size
ggplot(iris, aes(x=Sepal.Length, y=Species)) +
geom_boxplot(outlier.colour="red", outlier.shape=8,
outlier.size=4)
Change box plot line colors
Box plot line colors can be automatically controlled by the level variable :
# Change box plot line colors by groups
p<-ggplot(iris, aes(y=Sepal.Length, x=Species, color = Species)) +
geom_boxplot()
p
Change box plot fill colors
# Change box plot colors by groups
p<- ggplot(iris, aes(y=Sepal.Length, x=Species, fill= Species)) +
geom_boxplot()
p
p + coord_flip() # horizontal box plot
2.8. Facet Plots in ggplot2
Faceting in ggplot2 allows for visual comparisons across categories. Below are several insightful facet grid examples, demonstrating different types of visualizations that can benefit from faceting.
- Best for understanding distribution differences across categories.
- Faceted by Species for clearer separation of groups.
- A Categorical Variable for Faceting Faceting is used to create multiple subplots based on levels of a categorical variable. The variable should be discrete (categorical) or grouped continuous data.
- Facet Functions:
- facet_wrap(~ variable): Creates small multiples in a wrapped format.
- facet_grid(rows ~ cols): Creates a grid of plots based on row and column factors.
- Data & Aesthetic Mappings (aes()) A dataset that contains the categorical variable for faceting. The x and y variables should be mapped inside aes().
2.8.1. Faceted Histogram (Comparing Distributions Across Categories)
Question: How does the distribution of Sepal.Length vary across Species?
ggplot(iris, aes(x = Sepal.Length)) +
geom_histogram(color = "black", fill = "steelblue", bins = 20, alpha = 0.7) +
facet_grid(Species ~ .) + # Facet by Species
ggtitle("Faceted Histogram of Sepal Length by Species") +
xlab("Sepal Length") +
ylab("Count") +
theme_minimal()
Plot like this helps us to compare the distribution as follows:
Shape: Iris setosa exhibits a symmetric distribution, while versicolor and virginica show slight right skewness.
Measure of Center: Iris setosa has the shortest average sepal length, followed by versicolor, with virginica having the longest.
Measure of Dispersion: Iris virginica displays the greatest variability in sepal length, whereas setosa shows the least.
2.8.2. Faceted Scatter Plot (Relationships Across Categories)
- Best for visualizing category-wise correlations.
- Helps detect trends, clusters, or outliers within groups.
Question: How does the relationship between Sepal.Length and Sepal.Width differ across Species?
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
geom_point(size = 5) +
facet_wrap(~ Species) + # Creates separate scatter plots per Species
ggtitle("Sepal Length vs Sepal Width Across Species") +
xlab("Sepal Length") +
ylab("Sepal Width") +
theme_minimal()+
theme(
axis.line = element_line(colour = "black", size = 0.5), # Make xy-axes black, size is thickness
plot.title = element_text(hjust = 0.5, size = 18, face = "bold") # Centered bold title
)
2.8.3. Faceted Line Chart (Time Series Data Across Groups)
- Best for visualizing trends in different groups over a continuous variable.
Question: How does Sepal.Width trend with Sepal.Length across Iris-Species?
str(iris)## 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
geom_line() +
facet_wrap(~ Species) + # One line chart per Species
ggtitle("Sepal.Length vs Sepal.Width across Iris-Species") +
xlab("Sepal.Length") +
ylab("Sepal.Width") +
theme_minimal()
2.8.4. Faceted Box Plot (Comparing Distributions Side-by-Side)
- Best for comparing distributions across two categorical variables.
Question: How does mpg vary across transmission types and cylinders?
ggplot(mtcars, aes(x = factor(cyl), y = mpg, fill = factor(am))) +
geom_boxplot() +
facet_grid(. ~ am) + # Faceted by transmission (am: 0 = Automatic, 1 = Manual)
scale_fill_manual(values = c("skyblue", "tomato")) + # Custom colors
ggtitle("MPG Distribution Across Cylinders and Transmission Types") +
xlab("Number of Cylinders") +
ylab("Miles Per Gallon (MPG)") +
theme_minimal()
data("diamonds")ggplot(diamonds, aes(x = clarity, y = price, fill = cut)) +
geom_boxplot() +
facet_wrap(~ cut) +
ggtitle("Diamond Price by Clarity Across Cut Levels") +
xlab("Clarity") +
ylab("Price (USD)") +
theme_minimal() +
theme(legend.position = "none")
2.8.5. Faceted Density Plot (Comparing Density Distributions)
- Best for comparing smooth distributions instead of histograms.
Question: How does the density of Sepal.Length vary across different Species?
ggplot(iris, aes(x = Sepal.Length, fill = Species)) +
geom_density(alpha = 0.5) +
facet_wrap(~ Species) + # Separate density plots for each transmission type
ggtitle("Sepal.Length Density Across Species Types") +
xlab("Sepal.Length") +
ylab("Density") +
theme_minimal()
2.8.6. Faceted Bar Chart (Comparing Categorical Counts)
- Best for visualizing frequency differences across categories.
- Faceted by cyl to separate comparisons.
Question: How do gear counts vary across different cylinder types?
ggplot(mtcars, aes(x = factor(gear), fill = factor(cyl))) +
geom_bar(position = "dodge") +
facet_wrap(~ cyl) + # Separate bar charts per cylinder type
ggtitle("Gear Count Across Cylinders") +
xlab("Number of Gears") +
ylab("Count") +
theme_minimal()